A method for detecting cloud service uptime for a cloud service on a cloud service provider, by means of a cognitive agent. The method includes receiving, by the cognitive agent, a cloud service request from a client, and determining the cloud service uptime based on a cloud service uptime model. The method further includes preventing the cloud service request from the client if it is determined that the cloud service uptime exceeds a given threshold, and transmitting, by the cognitive agent, cloud service uptime details to the client. The method further includes requesting a provisioning of a cloud service if it is determined that the cloud service uptime does not exceed the given threshold. The method further includes training, by the cognitive agent, the cloud service uptime model based on a dynamic polling of the cloud service, wherein the cloud service uptime model provides an optimal response to the client.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A computer-implemented method for detecting a cloud service uptime for a public cloud service on a cloud service provider, by means of a cognitive agent, comprising: receiving, by the cognitive agent, a cloud service request from a client; detecting scheduled and unscheduled downtime of the public cloud service, via the cognitive agent, by polling the public cloud service on the cloud service provider at a configurable, or dynamic interval, to know a root cause of one or more cloud service failures in order to avoid multiple round trips to the public cloud; informing the client, by the cognitive agent, about an availability of the public cloud service before the public cloud service is actually used; optimizing a caching functionality of the cognitive agent in order to provide an optimal time to re-send the cloud service request from the client, wherein the cognitive agent presents a pattern on how frequent the public cloud service is used, and for how long; determining the cloud service uptime based on a cloud service uptime model, wherein the cognitive agent utilizes cloud service uptime details to train the cloud service uptime model; preventing the cloud service request from the client if the cloud service uptime model determines that the cloud service uptime exceeds a given threshold; transmitting, by the cognitive agent, the optimal time to re-send the cloud service request, from the client, according to the cloud service uptime model; and sending, by the cognitive agent, an automated cloud service request to the cloud service provider at the optimal time, according to the cloud service uptime model.
2. The computer-implemented method of claim 1 , further comprising: requesting a provisioning of a cloud service if it is determined that the cloud service uptime does not exceed the given threshold.
3. The computer-implemented method of claim 1 , further comprising: suggesting, by the cognitive agent, a later time for provisioning a service, based on the cloud service uptime model.
4. The computer-implemented method of claim 1 , further comprising: training, by the cognitive agent, the cloud service uptime model based on a dynamic polling of the cloud service, wherein the cloud service uptime model provides an optimal response to the client.
5. The computer-implemented method of claim 1 , wherein the cognitive agent transmits, on a continuous or an on-demand basis, the cloud service uptime details to the cloud client by means of a binary bit payload containing an error code, if it is determined that the cloud service uptime exceeds the given threshold.
6. The computer-implemented method of claim 1 , wherein the cloud service uptime details comprise a typical use of the cloud service, a typical duration of the cloud service, and a typical time of day that the cloud service is in use on the cloud service provider.
7. The computer-implemented method of claim 4 , wherein dynamic polling of the cloud service further comprises: polling one or more different cloud service types at one or more configurable intervals for a typical duration, frequency, and use.
8. A computer program product, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising: receiving, by the cognitive agent, a cloud service request from a client; detecting scheduled and unscheduled downtime of the public cloud service, via the cognitive agent, by polling the public cloud service on the cloud service provider at a configurable, or dynamic interval, to know a root cause of one or more cloud service failures in order to avoid multiple round trips to the public cloud; informing the client, by the cognitive agent, about an availability of the public cloud service before the public cloud service is actually used; optimizing a caching functionality of the cognitive agent in order to provide an optimal time to re-send the cloud service request from the client, wherein the cognitive agent presents a pattern on how frequent the public cloud service is used, and for how long; determining the cloud service uptime based on a cloud service uptime model, wherein the cognitive agent utilizes cloud service uptime details to train the cloud service uptime model; preventing the cloud service request from the client if the cloud service uptime model determines that the cloud service uptime exceeds a given threshold; transmitting, by the cognitive agent, the optimal time to re-send the cloud service request, from the client, according to the cloud service uptime model; and sending, by the cognitive agent, an automated cloud service request to the cloud service provider at the optimal time, according to the cloud service uptime model.
9. The computer program product of claim 8 , further comprising: requesting a provisioning of a cloud service if it is determined that the cloud service uptime does not exceed the given threshold.
10. The computer program product of claim 8 , further comprising: suggesting, by the cognitive agent, a later time for provisioning a service, based on the cloud service uptime model.
11. The computer program product of claim 8 , further comprising: training, by the cognitive agent, the cloud service uptime model based on a dynamic polling of the cloud service, wherein the cloud service uptime model provides an optimal response to the client.
12. The computer program product of claim 8 , wherein the cognitive agent transmits, on a continuous or an on-demand basis, the cloud service uptime details to the cloud client by means of a binary bit payload containing an error code, if it is determined that the cloud service uptime exceeds the given threshold.
13. The computer program product of claim 8 , wherein the cloud service uptime details comprise a typical use of the cloud service, a typical duration of the cloud service, and a typical time of day that the cloud service is in use on the cloud service provider.
14. The computer program product of claim 11 , wherein dynamic polling of the cloud service further comprises: polling one or more different cloud service types at one or more configurable intervals for a typical duration, frequency, and use.
15. A computer system, comprising: one or more computer devices each having one or more processors and one or more tangible storage devices; and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the plurality of program instructions comprising instructions for: receiving, by the cognitive agent, a cloud service request from a client; detecting scheduled and unscheduled downtime of the public cloud service, via the cognitive agent, by polling the public cloud service on the cloud service provider at a configurable, or dynamic interval, to know a root cause of one or more cloud service failures in order to avoid multiple round trips to the public cloud; informing the client, by the cognitive agent, about an availability of the public cloud service before the public cloud service is actually used; optimizing a caching functionality of the cognitive agent in order to provide an optimal time to re-send the cloud service request from the client, wherein the cognitive agent presents a pattern on how frequent the public cloud service is used, and for how long; determining the cloud service uptime based on a cloud service uptime model, wherein the cognitive agent utilizes cloud service uptime details to train the cloud service uptime model; preventing the cloud service request from the client if the cloud service uptime model determines that the cloud service uptime exceeds a given threshold; transmitting, by the cognitive agent, the optimal time to re-send the cloud service request, from the client, according to the cloud service uptime model; and sending, by the cognitive agent, an automated cloud service request to the cloud service provider at the optimal time, according to the cloud service uptime model.
16. The computer system of claim 15 , further comprising: requesting a provisioning of a cloud service if it is determined that the cloud service uptime does not exceed the given threshold.
17. The computer system of claim 15 , further comprising: suggesting, by the cognitive agent, a later time for provisioning a service, based on the cloud service uptime model.
18. The computer system of claim 15 , further comprising: training, by the cognitive agent, the cloud service uptime model based on a dynamic polling of the cloud service, wherein the cloud service uptime model provides an optimal response to the client.
19. The computer system of claim 15 , wherein the cognitive agent transmits, on a continuous or an on-demand basis, the cloud service uptime details to the cloud client by means of a binary bit payload containing an error code, if it is determined that the cloud service uptime exceeds the given threshold.
20. The computer system of claim 15 , wherein the cloud service uptime details comprise a typical use of the cloud service, a typical duration of the cloud service, and a typical time of day that the cloud service is in use on the cloud service provider.
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November 3, 2017
March 30, 2021
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